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Publishing Language: Chinese

Spatiotemporal variations of plastic-mulched cropland in Hexi Corridor using multi-source remote sensing data

Lei CHENG1, Chenhao FU1, Haiyang XI2,3, Liwei CHANG1, Shiqiong LI1, Shujing QIN1( )
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
The Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Key Lab Ecohydrology Inland River Basin, Lanzhou 730000, China
University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract

Plastic mulch has been widely used to increase the soil temperature with the constant moisture for the high crop yields in the arid regions in northwestern China. Particularly, there has also the application in maize, wheat, and cotton cultivation in Hexi Corridor since the last century. Nevertheless, the mulch residues have significantly damaged the soil structure, leading to environmental degradation. Therefore, it is very necessary for the spatial and temporal distribution of plastic mulch, in order to quantify the impact on the local environment. However, the wide region and the complex interlocking surface types have posed a great challenge to the spatial and temporal distribution of plastic mulched cropland (PMC) from the field work. Fortunately, large-scale ground objects can be extracted with the development of remote sensing. However it is still lacking in the acquisition and processing of remote sensing data. This study aims to extract the spatiotemporal PMC variations in Hexi Corridor from 2013 to 2022. The multi-source remote sensing data was first collected using Google Earth Engine, including Sentinel-2, Landsat 8, Landsat 7, and MODIS. A time window of PMC extraction was also proposed using the variance of the Normalized Difference Vegetation Index (NDVI) within one year. Then, the Timely Plastic-mulched cropland Extraction Method (TPEM) was applied to the complex and variable ground objects for the extraction of the PMC area. About 500 sample points were randomly selected and then labelled as the PMC and non-PMC for the accuracy assessment. The Overall Accuracy (OA), User Accuracy (UA), Quantity Disagreement (QD), and Allocation Disagreement (AD) were also used to evaluate the performance of TPEM. Finally, the spatiotemporal pattern of PMC was analyzed to explore the potential controls on the interannual variation in the PMC area using Water Yearbook Bulletin data. The results showed that: 1) The best time window was the first half of April for the PMC extraction, which also coincided with the sowing of major local crops. The half month before and after this period was also beneficial to extracting the PMC information. 2) The TPEM reached 80% overall accuracy with the QD less than 4%. The error was attributed to the disagreement between the sample points and the real PMC area. 3) The average PMC area was about 383000 hm2, of which about 55.6% was in the Heihe Basin. The PMC was located near the inland river systems. Approximately 41.9% of the cropland used plastic mulch, while the PMC area decreased by 32.4% in the last decade. Meanwhile, there was a 46.0% increase in the water-saving irrigation area, whereas, there was a 14.9% decrease in the irrigation water from 2013 to 2019, indicating the significant correlation with the decrease in the PMC area. In conclusion, the application of plastic mulch was ever decreasing with the development of water-saving irrigation. This finding can be an essential reference for the survey of agricultural resources in the Northwest Arid Region of China.

CLC number: S127 Document code: A

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Transactions of the Chinese Society of Agricultural Engineering
Pages 124-131

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Cite this article:
CHENG L, FU C, XI H, et al. Spatiotemporal variations of plastic-mulched cropland in Hexi Corridor using multi-source remote sensing data. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(17): 124-131. https://doi.org/10.11975/j.issn.1002-6819.202303209

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Received: 21 March 2023
Revised: 20 August 2023
Published: 15 September 2023
© Chinese Society of Agricultural Engineering 2023